Supply Chain Analysis

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@kaggle.harshsingh2209_supply_chain_analysis

Table List

1 table(25.72 KB)

Supply Chain Data

@kaggle.harshsingh2209_supply_chain_analysis.supply_chain_data

25.72 KB
100 rows
24 columns

product_type Product Type	sku SKU	price Price	availability Availability	number_of_products_sold Number Of Products Sold	revenue_generated Revenue Generated	customer_demographics Customer Demographics	stock_levels Stock Levels	lead_times Lead Times	order_quantities Order Quantities	shipping_times Shipping Times	shipping_carriers Shipping Carriers	shipping_costs Shipping Costs	supplier_name Supplier Name	location Location	lead_time Lead Time	production_volumes Production Volumes	manufacturing_lead_time Manufacturing Lead Time	manufacturing_costs Manufacturing Costs	inspection_results Inspection Results	defect_rates Defect Rates	transportation_modes Transportation Modes	routes Routes	costs Costs
haircare	SKU0	69.80800554211577	55	802	8661.996792392383	Non-binary	58	7	96	4	Carrier B	2.956572139430807	Supplier 3	Mumbai	29	215	29	46.27987924050832	Pending	0.2264103608499251	Road	Route B	187.75207545920392
skincare	SKU1	14.84352327508434	95	736	7460.900065445849	Female	53	30	37	2	Carrier A	9.71657477143131	Supplier 3	Mumbai	23	517	30	33.61676895373	Pending	4.854068026388706	Road	Route B	503.0655791496692
haircare	SKU2	11.319683293090566	34	8	9577.74962586873	Unknown	1	10	88	2	Carrier B	8.054479261732155	Supplier 1	Mumbai	12	971	27	30.688019348284204	Pending	4.580592619199229	Air	Route C	141.92028177151906
skincare	SKU3	61.163343016437736	68	83	7766.836425685233	Non-binary	23	13	59	6	Carrier C	1.7295685635434288	Supplier 5	Kolkata	24	937	18	35.62474139712503	Fail	4.74664862064775	Rail	Route A	254.77615921928663
skincare	SKU4	4.805496036345893	26	871	2686.505151567447	Non-binary	5	3	56	8	Carrier A	3.890547915870672	Supplier 1	Delhi	5	414	3	92.06516059871284	Fail	3.145579522833002	Air	Route A	923.4406317119222
haircare	SKU5	1.6999760138659377	87	147	2828.348745975759	Non-binary	90	27	66	3	Carrier B	4.444098864382293	Supplier 4	Bangalore	10	104	17	56.7664755574318	Fail	2.7791935115711617	Road	Route A	235.4612367355375
skincare	SKU6	4.078332863107945	48	65	7823.476559531737	Male	11	15	58	8	Carrier C	3.880763302952003	Supplier 3	Kolkata	14	314	24	1.0850685695870688	Pending	1.0009106193041355	Sea	Route A	134.36909686103172
cosmetics	SKU7	42.95838438246007	59	426	8496.103813089838	Female	93	17	11	1	Carrier B	2.3483387844177805	Supplier 4	Bangalore	22	564	1	99.46610860359912	Fail	0.3981771868506506	Road	Route C	802.0563118175586
cosmetics	SKU8	68.71759674852733	78	150	7517.363210631127	Female	5	10	15	7	Carrier C	3.4047338570830266	Supplier 4	Mumbai	13	769	8	11.423027139565695	Pending	2.709862691109961	Sea	Route B	505.55713422546415
skincare	SKU9	64.01573294127854	35	980	4971.145987585558	Unknown	14	27	83	1	Carrier A	7.166645291048216	Supplier 2	Chennai	29	963	23	47.95760163495158	Pending	3.844614478767585	Rail	Route B	995.9294614986416


CREATE TABLE supply_chain_data (
  "product_type" VARCHAR,
  "sku" VARCHAR,
  "price" DOUBLE,
  "availability" BIGINT,
  "number_of_products_sold" BIGINT,
  "revenue_generated" DOUBLE,
  "customer_demographics" VARCHAR,
  "stock_levels" BIGINT,
  "lead_times" BIGINT,
  "order_quantities" BIGINT,
  "shipping_times" BIGINT,
  "shipping_carriers" VARCHAR,
  "shipping_costs" DOUBLE,
  "supplier_name" VARCHAR,
  "location" VARCHAR,
  "lead_time" BIGINT,
  "production_volumes" BIGINT,
  "manufacturing_lead_time" BIGINT,
  "manufacturing_costs" DOUBLE,
  "inspection_results" VARCHAR,
  "defect_rates" DOUBLE,
  "transportation_modes" VARCHAR,
  "routes" VARCHAR,
  "costs" DOUBLE
);